{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X token train shape: (10244,)\n",
      "X token test shape: (2560,)\n",
      "X morph train shape: (10244,)\n",
      "X morph test shape: (2560,)\n"
     ]
    }
   ],
   "source": [
    "import codecs\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import numpy as np\n",
    "\n",
    "def load_data(filename):\n",
    "    data = list(codecs.open(filename, 'r', 'utf-8').readlines())\n",
    "    x, y = zip(*[d.strip().split('\\t') for d in data])\n",
    "    x = np.asarray(list(x))\n",
    "    y = to_categorical(y, 3)\n",
    "    \n",
    "    return x, y\n",
    "    \n",
    "x_token_train, y_token_train = load_data('data/token_train.tsv')\n",
    "x_token_test, y_token_test = load_data('data/token_test.tsv')\n",
    "x_morph_train, y_morph_train = load_data('data/morph_train.tsv')\n",
    "x_morph_test, y_morph_test = load_data('data/morph_test.tsv')\n",
    "\n",
    "print('X token train shape: {}'.format(x_token_train.shape))\n",
    "print('X token test shape: {}'.format(x_token_test.shape))\n",
    "\n",
    "print('X morph train shape: {}'.format(x_morph_train.shape))\n",
    "print('X morph test shape: {}'.format(x_morph_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare\n",
    "Convert text (train & test) to sequences and pad to requested document length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X token train shape: (10244, 300)\n",
      "X token test shape: (2560, 300)\n",
      "X morph train shape: (10244, 300)\n",
      "X morph test shape: (2560, 300)\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing import text, sequence\n",
    "\n",
    "def tokenizer(x_train, x_test, vocabulary_size, char_level):\n",
    "    tokenize = text.Tokenizer(num_words=vocabulary_size, \n",
    "                              char_level=char_level,\n",
    "                              filters='')\n",
    "    tokenize.fit_on_texts(x_train)  # only fit on train\n",
    "    x_train = tokenize.texts_to_sequences(x_train)\n",
    "    x_test = tokenize.texts_to_sequences(x_test)\n",
    "    \n",
    "    return x_train, x_test\n",
    "\n",
    "def pad(x_train, x_test, max_document_length):\n",
    "    x_train = sequence.pad_sequences(x_train, maxlen=max_document_length, padding='post', truncating='post')\n",
    "    x_test = sequence.pad_sequences(x_test, maxlen=max_document_length, padding='post', truncating='post')\n",
    "    \n",
    "    return x_train, x_test\n",
    "\n",
    "vocabulary_size = 5000\n",
    "\n",
    "x_token_train, x_token_test = tokenizer(x_token_train, x_token_test, vocabulary_size, True)\n",
    "x_morph_train, x_morph_test = tokenizer(x_morph_train, x_morph_test, vocabulary_size, True)\n",
    "\n",
    "max_document_length = 300\n",
    "\n",
    "x_token_train, x_token_test = pad(x_token_train, x_token_test, max_document_length)\n",
    "x_morph_train, x_morph_test = pad(x_morph_train, x_morph_test, max_document_length)\n",
    "\n",
    "print('X token train shape: {}'.format(x_token_train.shape))\n",
    "print('X token test shape: {}'.format(x_token_test.shape))\n",
    "\n",
    "print('X morph train shape: {}'.format(x_morph_train.shape))\n",
    "print('X morph test shape: {}'.format(x_morph_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token OOV ratio: 0.03508771929824561 (8 out of 228)\n",
      "Morph OOV ratio: 0.03508771929824561 (8 out of 228)\n"
     ]
    }
   ],
   "source": [
    "print('Token OOV ratio: {} ({} out of 228)'.format(np.count_nonzero(x_token_test == 228)/228, np.count_nonzero(x_token_test == 228)))\n",
    "print('Morph OOV ratio: {} ({} out of 228)'.format(np.count_nonzero(x_morph_test == 228)/228, np.count_nonzero(x_morph_test == 228)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_loss_and_accuracy(history):\n",
    "    \n",
    "    fig, axs = plt.subplots(1, 2, sharex=True)\n",
    "    \n",
    "    axs[0].plot(history.history['loss'])\n",
    "    axs[0].plot(history.history['val_loss'])\n",
    "    axs[0].set_title('Model Loss')\n",
    "    axs[0].legend(['Train', 'Validation'], loc='upper left')\n",
    "    \n",
    "    axs[1].plot(history.history['acc'])\n",
    "    axs[1].plot(history.history['val_acc'])\n",
    "    axs[1].set_title('Model Accuracy')\n",
    "    axs[1].legend(['Train', 'Validation'], loc='upper left')\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import required modules from Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, Input, Concatenate\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import LSTM, Bidirectional\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.pooling import MaxPool1D\n",
    "from keras.layers import BatchNormalization\n",
    "from keras import optimizers\n",
    "from keras import metrics\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropout_keep_prob = 0.5\n",
    "embedding_size = 300\n",
    "batch_size = 50\n",
    "lr = 1e-4\n",
    "dev_size = 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/10\n",
      "8195/8195 [==============================] - 1s 133us/step - loss: 4.6573 - acc: 0.5634 - val_loss: 3.4261 - val_acc: 0.6837\n",
      "Epoch 2/10\n",
      "8195/8195 [==============================] - 1s 90us/step - loss: 3.9497 - acc: 0.6367 - val_loss: 3.3253 - val_acc: 0.6886\n",
      "Epoch 3/10\n",
      "8195/8195 [==============================] - 1s 96us/step - loss: 3.7765 - acc: 0.6476 - val_loss: 3.1227 - val_acc: 0.6950\n",
      "Epoch 4/10\n",
      "8195/8195 [==============================] - 1s 81us/step - loss: 3.7338 - acc: 0.6401 - val_loss: 3.2283 - val_acc: 0.7013\n",
      "Epoch 5/10\n",
      "8195/8195 [==============================] - 1s 87us/step - loss: 3.4944 - acc: 0.6491 - val_loss: 2.9340 - val_acc: 0.7003\n",
      "Epoch 6/10\n",
      "8195/8195 [==============================] - 1s 86us/step - loss: 3.4322 - acc: 0.6454 - val_loss: 2.6599 - val_acc: 0.7013\n",
      "Epoch 7/10\n",
      "8195/8195 [==============================] - 1s 86us/step - loss: 3.2137 - acc: 0.6420 - val_loss: 2.4780 - val_acc: 0.6920\n",
      "Epoch 8/10\n",
      "8195/8195 [==============================] - 1s 90us/step - loss: 3.1566 - acc: 0.6459 - val_loss: 2.4656 - val_acc: 0.7013\n",
      "Epoch 9/10\n",
      "8195/8195 [==============================] - 1s 108us/step - loss: 3.0975 - acc: 0.6428 - val_loss: 2.4278 - val_acc: 0.7072\n",
      "Epoch 10/10\n",
      "8195/8195 [==============================] - 1s 85us/step - loss: 2.9606 - acc: 0.6515 - val_loss: 2.2618 - val_acc: 0.7062\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19004d6aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 0s 28us/step\n",
      "\n",
      "Accurancy: 0.6938\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Dense(100)(text_input)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.4f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/Linear-Token-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/10\n",
      "8195/8195 [==============================] - 1s 118us/step - loss: 4.3945 - acc: 0.5639 - val_loss: 3.2610 - val_acc: 0.6925\n",
      "Epoch 2/10\n",
      "8195/8195 [==============================] - 0s 60us/step - loss: 3.8549 - acc: 0.6339 - val_loss: 3.2650 - val_acc: 0.6974\n",
      "Epoch 3/10\n",
      "8195/8195 [==============================] - 1s 69us/step - loss: 3.7139 - acc: 0.6430 - val_loss: 3.0271 - val_acc: 0.6959\n",
      "Epoch 4/10\n",
      "8195/8195 [==============================] - 0s 57us/step - loss: 3.5929 - acc: 0.6361 - val_loss: 2.9381 - val_acc: 0.6989\n",
      "Epoch 5/10\n",
      "8195/8195 [==============================] - 1s 66us/step - loss: 3.4279 - acc: 0.6482 - val_loss: 2.8578 - val_acc: 0.6950\n",
      "Epoch 6/10\n",
      "8195/8195 [==============================] - 0s 56us/step - loss: 3.3378 - acc: 0.6392 - val_loss: 2.7722 - val_acc: 0.6959\n",
      "Epoch 7/10\n",
      "8195/8195 [==============================] - 0s 54us/step - loss: 3.2074 - acc: 0.6430 - val_loss: 2.5296 - val_acc: 0.6901\n",
      "Epoch 8/10\n",
      "8195/8195 [==============================] - 0s 57us/step - loss: 3.0877 - acc: 0.6473 - val_loss: 2.5663 - val_acc: 0.6959\n",
      "Epoch 9/10\n",
      "8195/8195 [==============================] - 0s 54us/step - loss: 3.0295 - acc: 0.6487 - val_loss: 2.3091 - val_acc: 0.6964\n",
      "Epoch 10/10\n",
      "8195/8195 [==============================] - 0s 54us/step - loss: 2.9823 - acc: 0.6427 - val_loss: 2.1973 - val_acc: 0.7023\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x190052654a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 0s 20us/step\n",
      "\n",
      "Accurancy: 0.6871\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Dense(100)(text_input)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.4f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/Linear-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/5\n",
      "8195/8195 [==============================] - 963s 118ms/step - loss: 0.6787 - acc: 0.7087 - val_loss: 0.6599 - val_acc: 0.7091\n",
      "Epoch 2/5\n",
      "8195/8195 [==============================] - 899s 110ms/step - loss: 0.6171 - acc: 0.7451 - val_loss: 0.5897 - val_acc: 0.7438\n",
      "Epoch 3/5\n",
      "8195/8195 [==============================] - 1610s 196ms/step - loss: 0.5314 - acc: 0.7863 - val_loss: 0.5533 - val_acc: 0.7589\n",
      "Epoch 4/5\n",
      "8195/8195 [==============================] - 1429s 174ms/step - loss: 0.4513 - acc: 0.8253 - val_loss: 0.5300 - val_acc: 0.7823\n",
      "Epoch 5/5\n",
      "8195/8195 [==============================] - 968s 118ms/step - loss: 0.3758 - acc: 0.8630 - val_loss: 0.4583 - val_acc: 0.8199\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x190052144e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 80s 31ms/step\n",
      "\n",
      "Accurancy: 0.824\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 5\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "convs = []\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "for fsz in [10, 30]:\n",
    "    conv = Conv1D(128, fsz, padding='valid', activation='relu')(x)\n",
    "    pool = MaxPool1D()(conv)\n",
    "    convs.append(pool)\n",
    "x = Concatenate(axis=1)(convs)\n",
    "x = Flatten()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/CNN-Token-{:.3f}.h5'.format((scores[1] * 100)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/5\n",
      "8195/8195 [==============================] - 879s 107ms/step - loss: 0.6855 - acc: 0.7013 - val_loss: 0.6589 - val_acc: 0.7096\n",
      "Epoch 2/5\n",
      "8195/8195 [==============================] - 812s 99ms/step - loss: 0.6271 - acc: 0.7353 - val_loss: 0.5972 - val_acc: 0.7496\n",
      "Epoch 3/5\n",
      "8195/8195 [==============================] - 807s 98ms/step - loss: 0.5391 - acc: 0.7780 - val_loss: 0.5406 - val_acc: 0.7716\n",
      "Epoch 4/5\n",
      "8195/8195 [==============================] - 802s 98ms/step - loss: 0.4715 - acc: 0.8148 - val_loss: 0.4960 - val_acc: 0.7916\n",
      "Epoch 5/5\n",
      "8195/8195 [==============================] - 802s 98ms/step - loss: 0.4075 - acc: 0.8461 - val_loss: 0.5182 - val_acc: 0.7897\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x190050bab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 78s 30ms/step\n",
      "\n",
      "Accurancy: 0.7855\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "convs = []\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "for fsz in [10, 30]:\n",
    "    conv = Conv1D(128, fsz, padding='valid', activation='relu')(x)\n",
    "    pool = MaxPool1D()(conv)\n",
    "    convs.append(pool)\n",
    "x = Concatenate(axis=1)(convs)\n",
    "x = Flatten()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.4f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/CNN-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/7\n",
      "8195/8195 [==============================] - 272s 33ms/step - loss: 0.8362 - acc: 0.6589 - val_loss: 0.7334 - val_acc: 0.6603\n",
      "Epoch 2/7\n",
      "8195/8195 [==============================] - 268s 33ms/step - loss: 0.7338 - acc: 0.6835 - val_loss: 0.7153 - val_acc: 0.7003\n",
      "Epoch 3/7\n",
      "8195/8195 [==============================] - 255s 31ms/step - loss: 0.7200 - acc: 0.6979 - val_loss: 0.7114 - val_acc: 0.6994\n",
      "Epoch 4/7\n",
      "8195/8195 [==============================] - 252s 31ms/step - loss: 0.7188 - acc: 0.6995 - val_loss: 0.7151 - val_acc: 0.6994\n",
      "Epoch 5/7\n",
      "8195/8195 [==============================] - 254s 31ms/step - loss: 0.7155 - acc: 0.7007 - val_loss: 0.7100 - val_acc: 0.6999\n",
      "Epoch 6/7\n",
      "8195/8195 [==============================] - 251s 31ms/step - loss: 0.7119 - acc: 0.7015 - val_loss: 0.7092 - val_acc: 0.6999\n",
      "Epoch 7/7\n",
      "8195/8195 [==============================] - 252s 31ms/step - loss: 0.7103 - acc: 0.7014 - val_loss: 0.7094 - val_acc: 0.7008\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19006a67630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 20s 8ms/step\n",
      "\n",
      "Accurancy: 0.695\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 7\n",
    "lstm_units = 93\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = LSTM(units=lstm_units, return_sequences=True)(x)\n",
    "x = LSTM(units=lstm_units)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/LSTM-Token-{:.3f}.h5'.format((scores[1] * 100)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/7\n",
      "8195/8195 [==============================] - 253s 31ms/step - loss: 0.8080 - acc: 0.6534 - val_loss: 0.7334 - val_acc: 0.6593\n",
      "Epoch 2/7\n",
      "8195/8195 [==============================] - 252s 31ms/step - loss: 0.7272 - acc: 0.6704 - val_loss: 0.7250 - val_acc: 0.6593\n",
      "Epoch 3/7\n",
      "8195/8195 [==============================] - 252s 31ms/step - loss: 0.7142 - acc: 0.7014 - val_loss: 0.7073 - val_acc: 0.7091\n",
      "Epoch 4/7\n",
      "8195/8195 [==============================] - 253s 31ms/step - loss: 0.7039 - acc: 0.7049 - val_loss: 0.7041 - val_acc: 0.7028\n",
      "Epoch 5/7\n",
      "8195/8195 [==============================] - 251s 31ms/step - loss: 0.7050 - acc: 0.7053 - val_loss: 0.7071 - val_acc: 0.7038\n",
      "Epoch 6/7\n",
      "8195/8195 [==============================] - 251s 31ms/step - loss: 0.7037 - acc: 0.7070 - val_loss: 0.7047 - val_acc: 0.7042\n",
      "Epoch 7/7\n",
      "8195/8195 [==============================] - 253s 31ms/step - loss: 0.6972 - acc: 0.7093 - val_loss: 0.6922 - val_acc: 0.7135\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1900888fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 20s 8ms/step\n",
      "\n",
      "Accurancy: 0.706\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = LSTM(units=lstm_units, return_sequences=True)(x)\n",
    "x = LSTM(units=lstm_units)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/LSTM-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BiLSTM - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/4\n",
      "8195/8195 [==============================] - 646s 79ms/step - loss: 0.7930 - acc: 0.6696 - val_loss: 0.6746 - val_acc: 0.6999\n",
      "Epoch 2/4\n",
      "8195/8195 [==============================] - 647s 79ms/step - loss: 0.6525 - acc: 0.7174 - val_loss: 0.6345 - val_acc: 0.7125\n",
      "Epoch 3/4\n",
      "8195/8195 [==============================] - 647s 79ms/step - loss: 0.6233 - acc: 0.7236 - val_loss: 0.6086 - val_acc: 0.7291\n",
      "Epoch 4/4\n",
      "8195/8195 [==============================] - 657s 80ms/step - loss: 0.6046 - acc: 0.7407 - val_loss: 0.6129 - val_acc: 0.7365\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x190057c42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 47s 18ms/step\n",
      "\n",
      "Accurancy: 0.737\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 4\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Bidirectional(LSTM(units=lstm_units, return_sequences=True))(x)\n",
    "x = Bidirectional(LSTM(units=lstm_units))(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/BiLSTM-Token-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BiLSTM - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/4\n",
      "8195/8195 [==============================] - 666s 81ms/step - loss: 0.7960 - acc: 0.6636 - val_loss: 0.6901 - val_acc: 0.6789\n",
      "Epoch 2/4\n",
      "8195/8195 [==============================] - 662s 81ms/step - loss: 0.6700 - acc: 0.6990 - val_loss: 0.6409 - val_acc: 0.7160\n",
      "Epoch 3/4\n",
      "8195/8195 [==============================] - 661s 81ms/step - loss: 0.6261 - acc: 0.7254 - val_loss: 0.6234 - val_acc: 0.7189\n",
      "Epoch 4/4\n",
      "8195/8195 [==============================] - 660s 80ms/step - loss: 0.6072 - acc: 0.7304 - val_loss: 0.6061 - val_acc: 0.7384\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1901c3b3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 46s 18ms/step\n",
      "\n",
      "Accurancy: 0.731\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Bidirectional(LSTM(units=lstm_units, return_sequences=True))(x)\n",
    "x = Bidirectional(LSTM(units=lstm_units))(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/BiLSTM-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/6\n",
      "8195/8195 [==============================] - 148s 18ms/step - loss: 0.8018 - acc: 0.6542 - val_loss: 0.6827 - val_acc: 0.7101\n",
      "Epoch 2/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.7361 - acc: 0.6790 - val_loss: 0.6675 - val_acc: 0.7130\n",
      "Epoch 3/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.7016 - acc: 0.6985 - val_loss: 0.6436 - val_acc: 0.7130\n",
      "Epoch 4/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.6782 - acc: 0.7085 - val_loss: 0.6273 - val_acc: 0.7272\n",
      "Epoch 5/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.6321 - acc: 0.7348 - val_loss: 0.5946 - val_acc: 0.7413\n",
      "Epoch 6/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.5681 - acc: 0.7640 - val_loss: 0.5611 - val_acc: 0.7516\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1901e226f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 5s 2ms/step\n",
      "\n",
      "Accurancy: 0.746\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 6\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Flatten()(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/MLP-Token-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/6\n",
      "8195/8195 [==============================] - 148s 18ms/step - loss: 0.8144 - acc: 0.6342 - val_loss: 0.6832 - val_acc: 0.7052\n",
      "Epoch 2/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.7343 - acc: 0.6786 - val_loss: 0.6664 - val_acc: 0.7111\n",
      "Epoch 3/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.6986 - acc: 0.6965 - val_loss: 0.6470 - val_acc: 0.7179\n",
      "Epoch 4/6\n",
      "8195/8195 [==============================] - 145s 18ms/step - loss: 0.6575 - acc: 0.7170 - val_loss: 0.6295 - val_acc: 0.7194\n",
      "Epoch 5/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.6160 - acc: 0.7378 - val_loss: 0.6030 - val_acc: 0.7335\n",
      "Epoch 6/6\n",
      "8195/8195 [==============================] - 146s 18ms/step - loss: 0.5639 - acc: 0.7667 - val_loss: 0.5822 - val_acc: 0.7452\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1901e264d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 5s 2ms/step\n",
      "\n",
      "Accurancy: 0.745\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Flatten()(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('char_saved_models/MLP-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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